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Optimal bidding of a hydropower producer insequential power markets with riskassessment: Stochastic programming approach
KTH, School of Electrical Engineering (EES), Electric power and energy systems. (EMReG)
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Short-term hydropower planning and bidding under uncertainty is a complicated task. The problem became more challenging with the liberalized market environment within the last two decades. Apart from this new reform taking place in the electricity market, the electricity market participants including hydropower producers experienced the second change in the form of intermittent wind power integration into power systems. Thus, previous decision support tools are not capable of fulfilling market participants’ expectations in the new competitive and highly uncertain environment. Intermittent power sources, namely wind power, increase the imbalances in the power system, which in turn increases the need of the regulating power sources. Being a flexible energy source, hydropower can provide regulating power. For this purpose, new hydropower planning and bidding models must be developed, capable of addressing uncertainties and the dynamics existing within market places.

In this dissertation, a set of new short-term hydropower planning and bidding models are developed for sequential electricity markets under price uncertainties. Developed stochastic coordinated hydropower planning and bidding tools can be classified into two classes, as models with exogenousand endogenous prices.

In the first class, developed coordinated bidding tools address the price uncertainties using scenario trees, which are built based on the distribution function of the unknown variables. Thus, the proposed coordinated bidding and planning tools consider all possible future prices and market outcomes together with the likelihood of these market outcomes. To reflect the continuously clearing nature of intra-day and real-time markets rolling planning is applied. In addition, models apply risk measures as another way to hedge against uncertain prices.

In the second class, hydropower stochastic strategic bidding models are developed using stochastic bi-level optimization methodology. Here market prices are calculated internally as dual variables of the load balance constraints in the lower level ED problems. To be able to solve the stochastic bilevel optimization problem, KKT optimality conditions are applied. By this transformation the problem is converted to a single-level stochastic program, which is simplified further using a corresponding discretization technique.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2016.
Series
TRITA-EE, ISSN 1653-5146 ; 2016:039
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-192628ISBN: 978-91-7595-890-3OAI: oai:DiVA.org:kth-192628DiVA: diva2:971410
Public defence
2016-10-14, K1, Teknikringen 56, Stockholm, 11:00 (English)
Opponent
Supervisors
Note

QC 20160922

Available from: 2016-09-22 Created: 2016-09-16 Last updated: 2016-09-22Bibliographically approved
List of papers
1. Short-term Hydropower Planning With Uncertain Wind Power Production
Open this publication in new window or tab >>Short-term Hydropower Planning With Uncertain Wind Power Production
2013 (English)In: Power and Energy Society General Meeting (PES), 2013 IEEE, IEEE conference proceedings, 2013, 6672693- p.Conference paper (Refereed)
Abstract [en]

The main purpose of this paper is to summarize the findings from simulating two stochastic short-term planning models for a price-taker hydropower producer. The first model is a two-stage stochastic linear programming problem. Profound sensitivity analysis is provided in terms of volatility in spot market prices and water inflow level. The results show that for the short-term hydropower planning problems the effect of considering price uncertainty in the stochastic model is higher compared to considering inflow level uncertainty. The second model is a two- stage stochastic linear programming problem. The model generates optimal bids to spot market considering real-time market price uncertainties. While simultaneously bidding to both markets, the results are not realistic. To make the bidding strategies more flexible and robust different approaches are modeled and assessed. Finally one of the approaches is suggested as the most applicable one.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013
Series
, IEEE Power and Energy Society General Meeting PESGM, ISSN 1944-9925
Keyword
Hydropower planning, Price takers, Short term planning, Wind power production
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-134309 (URN)10.1109/PESMG.2013.6672693 (DOI)000331874302094 ()2-s2.0-84893159354 (ScopusID)978-1-4799-1303-9 (ISBN)
Conference
2013 IEEE Power and Energy Society General Meeting, PES 2013; Vancouver, BC; Canada; 21 July 2013 through 25 July 2013
Note

QC 20131217

Available from: 2013-11-20 Created: 2013-11-20 Last updated: 2016-09-22Bibliographically approved
2. Optimal bidding of a profit-maximizing hydropowerproducer in day-ahead and real-time markets
Open this publication in new window or tab >>Optimal bidding of a profit-maximizing hydropowerproducer in day-ahead and real-time markets
2014 (English)In: Probabilistic Methods Applied to Power Systems (PMAPS), 2014 International Conference on, IEEE conference proceedings, 2014, 1-6 p.Conference paper (Refereed)
Abstract [en]

This paper develops a price-driven optimal bidding strategy to day-ahead and real-time markets for a profit maximizer hydro power producer. The electricity prices in different market places are unknown when the bidding takes place. The optimal bidding problem is modeled as a multi-stage stochastic program considering the market prices continuously clearing nature. Specifically for that purpose rolling planning is applied, which allow re-forecasting and re-dispatching according to the arrival of new information. The results have shown that, there is a value for hydropower producer to participate in real-time market.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-165032 (URN)10.1109/PMAPS.2014.6960589 (DOI)000358734100012 ()2-s2.0-84915748907 (ScopusID)978-1-4799-3561-1 (ISBN)
Conference
International Conference on Probabilistic Methods Applied to Power Systems (PMAPS),7-10 July 2014,Durham
Note

QC 20150522

Available from: 2015-04-21 Created: 2015-04-21 Last updated: 2016-09-22Bibliographically approved
3. Modeling Regime Switching in Day-ahead MarketPrices Using Markov Model
Open this publication in new window or tab >>Modeling Regime Switching in Day-ahead MarketPrices Using Markov Model
2016 (English)Conference paper (Refereed)
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-192884 (URN)
Conference
ISGT Europe
Note

QC 20160922

Available from: 2016-09-22 Created: 2016-09-22 Last updated: 2016-09-22Bibliographically approved
4. Hydropower Producer Day-ahead Market StrategicOffering Using Stochastic Bi-level Optimization
Open this publication in new window or tab >>Hydropower Producer Day-ahead Market StrategicOffering Using Stochastic Bi-level Optimization
2015 (English)In: Proceedings of the International MultiConference of Engineers and Computer Scientists,Hong Kong, 18-20 March, 2015, IAENG , 2015, Vol. 2Conference paper (Refereed)
Abstract [en]

This paper proposes a bi-level stochastic optimization problem (a Stackelberg game) to generate optimal bids for a profit maximizing hydropower producer and presents a mathematical approach to solve it. The first level represents the strategically acting hydropower producer also called a Stackelberg leader, while the second level represents the transmission system operator (TSO) also called a Stackelberg follower. To solve the bi-level stochastic optimization problem, the second level is replaced by its KKT (Karush-Kuhn-Tucker) optimality conditions, which results in a stochastic MPEC (mathematical program with equilibrium constraints). Finally, the stochastic MPEC is reformulated as a stochastic MILP (mixed integer linear program) using linearization and SOS1 variables (special ordered sets of type 1). Results are reported studying a small case, which point out the impact on the market outcomes when a hydropower producer behaves strategically.

Place, publisher, year, edition, pages
IAENG, 2015
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-165033 (URN)978-988-19253-2-9 (ISBN)
Conference
International MultiConference of Engineers and Computer Scientists,Hong Kong, 18-20 March, 2015
Note

QC 20150522

Available from: 2015-04-21 Created: 2015-04-21 Last updated: 2016-09-22Bibliographically approved
5. Coordinated production planning of risk-averse hydropower producer in sequential markets
Open this publication in new window or tab >>Coordinated production planning of risk-averse hydropower producer in sequential markets
2016 (English)In: International Studies in Religion and Society, ISSN 1530-1311, E-ISSN 2050-7038, Vol. 26, no 6, 1226-1243 p.Article in journal (Refereed) Accepted
Abstract [en]

This paper proposes a quadratic programming (QP) model for optimal coordinated production of a risk-averse hydropower producer. The day-ahead, intra-day and real-time markets are considered. A rolling planning approach is used to take advantage of sequential clearing of mentioned markets. The multi-period risk of trading in different markets is modelled as quadratic terms in the objective function. To cope with uncertain prices, three price forecasting techniques are used. The best forecasting technique is selected based on a designed Markov switch. The discrete behaviour of intra-day and real-time market prices are modelled as different Markov states. The proposed QP model is coded in gams (GAMS Development Corporation, Washington, DC, USA) platform and solved using the mosek (Mosek ApS, Copenhagen, Denmark) solver. An example of a three-reservoir system from a Swedish hydropower producer is used to examine the proposed QP model. The results show the economic gains from coordinated production planning in sequential markets.

Keyword
coordinated production planning, sequential markets, quadratic programming, risk
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-189943 (URN)10.1002/etep.2131 (DOI)000378569400005 ()2-s2.0-84945406815 (ScopusID)
Note

QC 20160728

Available from: 2016-07-28 Created: 2016-07-25 Last updated: 2016-09-22Bibliographically approved
6. The Coordinated Bidding of a Hydropower Producer in Three-Settlement Markets with Time-Dependent Risk Measure
Open this publication in new window or tab >>The Coordinated Bidding of a Hydropower Producer in Three-Settlement Markets with Time-Dependent Risk Measure
(English)Article in journal (Other academic) Submitted
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-192883 (URN)
Note

QCR 20160922

Available from: 2016-09-22 Created: 2016-09-22 Last updated: 2016-09-22Bibliographically approved
7. Optimal bidding of a hydropower producer in day-aheadmarket with explicit modeling of opportunity cost
Open this publication in new window or tab >>Optimal bidding of a hydropower producer in day-aheadmarket with explicit modeling of opportunity cost
(English)Article in journal (Other academic) Submitted
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-192886 (URN)
Note

QCR 20160922

Available from: 2016-09-22 Created: 2016-09-22 Last updated: 2016-09-22Bibliographically approved

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